IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v11y2020i3d10.1007_s13198-020-00960-5.html
   My bibliography  Save this article

Implementation of image colorization with convolutional neural network

Author

Listed:
  • Chetna Dabas

    (Jaypee Institute of Iinformation Technology)

  • Shikhar Jain

    (Jaypee Institute of Iinformation Technology)

  • Ashish Bansal

    (Jaypee Institute of Iinformation Technology)

  • Vaibhav Sharma

    (Jaypee Institute of Iinformation Technology)

Abstract

Huge amount of work is getting done on Image colorization worldwide. This research paper proposes a model for image colorization while making use of fully automatic Convolutional Neural Network. Image colorization processes a daunting task, and this research paper proposes a relevant model for the prediction of A and B models for LAB color space and it makes a direct use the lightness channel. In this work, a pre-trained VGG-16 network was used for semantically interpreting the concepts associated with images and coloring the images. In the proposed work, the convolutional layer has been fused with the max pooling layer (higher one) of the VGG network. Architecture of the proposed model has been presented. The experimentation has been carried out with varying objective functions. LaMem experimental dataset has been used in this work in order to validate the proposed model. The proposed model is evaluated and results are visualized by histograms for true and predicted images for RGB values. Further, the proposed model has been compared with the existing models and performs better in terms of execution times (in s) for different image sizes and the results are tabulated.

Suggested Citation

  • Chetna Dabas & Shikhar Jain & Ashish Bansal & Vaibhav Sharma, 2020. "Implementation of image colorization with convolutional neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 625-634, June.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:3:d:10.1007_s13198-020-00960-5
    DOI: 10.1007/s13198-020-00960-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-020-00960-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-020-00960-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    3. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    4. Sebastian Gehrmann & Franck Dernoncourt & Yeran Li & Eric T Carlson & Joy T Wu & Jonathan Welt & John Foote Jr. & Edward T Moseley & David W Grant & Patrick D Tyler & Leo A Celi, 2018. "Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.
    5. Jungyoon Kim & Jihye Lim, 2021. "A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    6. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    7. Yue Sun & Songmin Dai & Jide Li & Yin Zhang & Xiaoqiang Li, 2019. "Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps," Future Internet, MDPI, vol. 11(2), pages 1-12, February.
    8. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    9. Wenjuan Fan & Jingnan Liu & Shuwan Zhu & Panos M. Pardalos, 2020. "Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)," Annals of Operations Research, Springer, vol. 294(1), pages 567-592, November.
    10. Young Jae Kim & Seung Seog Han & Hee Joo Yang & Sung Eun Chang, 2020. "Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-9, June.
    11. Claus Zippel & Sabine Bohnet-Joschko, 2021. "Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    12. Dario Sipari & Betsy D. M. Chaparro-Rico & Daniele Cafolla, 2022. "SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    13. Mara Giavina-Bianchi & Raquel Machado de Sousa & Vitor Zago de Almeida Paciello & William Gois Vitor & Aline Lissa Okita & Renata Prôa & Gian Lucca dos Santos Severino & Anderson Alves Schinaid & Rafa, 2021. "Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-13, September.
    14. Jamil Ahmad & Abdul Khader Jilani Saudagar & Khalid Mahmood Malik & Waseem Ahmad & Muhammad Badruddin Khan & Mozaherul Hoque Abul Hasanat & Abdullah AlTameem & Mohammed AlKhathami & Muhammad Sajjad, 2022. "Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans," IJERPH, MDPI, vol. 19(1), pages 1-16, January.
    15. Rasheed Omobolaji Alabi & Alhadi Almangush & Mohammed Elmusrati & Ilmo Leivo & Antti Mäkitie, 2022. "Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication," IJERPH, MDPI, vol. 19(14), pages 1-13, July.
    16. Jordi Munoz-Muriedas, 2021. "Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    17. Magdalena K Sobol & Sarah A Finkelstein, 2018. "Predictive pollen-based biome modeling using machine learning," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-29, August.
    18. Andreas Fügener & Jörn Grahl & Alok Gupta & Wolfgang Ketter, 2022. "Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation," Information Systems Research, INFORMS, vol. 33(2), pages 678-696, June.
    19. Vidhya V. & Anjan Gudigar & U. Raghavendra & Ajay Hegde & Girish R. Menon & Filippo Molinari & Edward J. Ciaccio & U. Rajendra Acharya, 2021. "Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives," IJERPH, MDPI, vol. 18(12), pages 1-29, June.
    20. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:11:y:2020:i:3:d:10.1007_s13198-020-00960-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.